Bretveld, R.

Groothuis-Oudshoorn, C.G.M.

IJzerman, M.J.

Ruiter, J.W.

Siesling, S.

Vliegen, I.M.H.

Witteveen, A.

Witteveen, Annemieke

Groothuis-Oudshoorn, Catharina Gerarda Maria

Vliegen, Ingrid

IJzerman, Maarten Joost

Siesling, Sabine

NARCIS 

Towards personalized breast cancer follow-up: prediction model for recurrence and allocation of visits during 10 years of follow-up

University of Twente Research Information

Towards personalized breast cancer follow-up: prediction model for recurrence and allocation of visits during 10 years of follow-up A. Witteveen1,2, J.W. Ruiter1, R. Bretveld3, C.G.M. Groothuis-Oudshoorn1, I.M.H. Vliegen4, M.J. IJzerman1, S. Siesling1,31 Dept. of Health Technology and Services Research (HTSR), MIRA institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands   2 Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA   3 Dept. Research, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands   4 Dept. of Industrial Engineering and Business Information Systems (IEBIS), Centre for Healthcare Operations Improvement & Research, University of Twente, Enschede, the NetherlandsAim: to analyze recurrence patterns and definepredictive factors for locoregional recurrence (LRR) andsubsequent recurrences up to 10 years after the primarytumor.Follow-up in the Netherlands from 2012:Using risk thresholds, follow-up visits can be reallocatedbased on risk over the ten years following primarytreatment.Netherlands Cancer Registry (NCR):• Women diagnosed in 2003 with primary invasive breast cancer• No distant metastasis (DM), synchronous or previous tumors• Curatively treated in NLSurvival analysis: Predictors first, second and thirdrecurrenceBased on current follow-up: Determination lower riskthreshold and quantiles for intervals.Predictive factors for first recurrence:Most important predictors for all types (LRR/secondprimary/DM) of second recurrence were tumor size,surgery type and radiotherapy, with the effect of the lasttwo reversed compared to the first recurrence.As an example, three risks groups were made for low(>50, hormone therapy), medium (<50, hormonetherapy) and high (>50, no hormone therapy) risk.Intervals and threshold based on 5 visits in 5 year:Below this hazard, we ‘accept’ the risk.Given these thresholds, the medium risk group shouldreceive 2 follow-up visits, and the high 7 during the periodof ten years. The low risk group remained below thethreshold for all the ten years.This model can be used to identify patients with a low orhigh risk to personalize follow-up after breast cancer,develop a decision support tool and allocate resourcesefficiently over the whole follow-up period.More information:Annemieke Witteveen, MScPhD candidatewww.utwente.nl/influenceA.Witteveen@utwente.nlBACKGROUND PATIENTS & METHODSRESULTSCONCLUSIONSYear 1 Year 2 Year 3 Year 4 Year 5Mammography and physical examination5.7% 6.4%87.9%SP LRR No reccurrence DM3.9%14.4%56.4%25.3% 5.4% 8.1%37.8%48.6%AgeSizeGrade of differentiationNodal involvementHormone statusSurgery typeRadiotherapyChemotherapyHormone therapy1st event                         2nd event                  3rd event   Cumulative hazard of complete population after 5 years: 0.0448Equal hazard intervals: 0.00896 per visit N=8,035Hazard of complete population after 5 year: 0.0068 Lower risk threshold

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Towards personalized breast cancer follow-up:prediction model for recurrence and allocation of visits during 10 years of follow-up

Towards personalized breast cancer follow-up: prediction model for recurrence and allocation of visits during 10 years of follow-up

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University of Twente Research Information